Table 4.

Results of the predictive modeling for the clinical features. XGBoost: eXtreme Gradient Boosting; LSVM: Lagrangian Support Vector Machine; Quest: Random Trees, and Quick, Unbiased, Efficient Statistical Tree; MLP-NN: multiplayer layer perceptron neural network; RBF-NN: radial basis function neural network

Algorithm		Accuracy
	Training
Random Trees		95.46
XGBoost Tree		100
LSVM		89.58
SVM		88.00
CHAID		89.79
MLP-NN		90.4
RBF-NN		87.4
	Testing
Random Trees		89.74
XGBoost Tree		90.49
LSVM		83.84
SVM		90.17
CHAID		85.46
MLP-NN		82.6
RBF-NN		91.5